Abstract. This paper studies how an operator with limited situational awareness can collaborate with a swarm of simulated robots. The robots are distributed in an environment with wall obstructions. They aggregate autonomously but are unable to form a single cluster due to the obstructions. The operator lacks the bird's-eye perspective, but can interact with one robot at a time, and influence the behavior of other nearby robots. We conducted a series of experiments. They show that untrained participants had marginal influence on the performance of the swarm. Expert participants succeeded in aggregating 85% of the robots while untrained participants, with bird's-eye view, succeeded in aggregating 90%. This demonstrates that the controls are sufficient for operators to aid the autonomous robots in the completion of the task and that lack of situational awareness is the main difficulty. An analysis of behavioral differences reveals that trained operators learned to gain superior situational awareness.
Abstract. We study how a human operator can guide a swarm of robots when transporting a large object through an environment with obstacles. The operator controls a leader robot that influences the other robots of the swarm. Follower robots push the object only if they have no line of sight of the leader. The leader represents a way point that the object should reach. By changing its position over time, the operator effectively guides the transporting robots towards the final destination. The operator uses the Google Glass device to interact with the swarm. Communication can be achieved via either touch or voice commands and the support of a graphical user interface. Experimental results with 20 physical e-puck robots show that the human-robot interaction allows the swarm to transport the object through a complex environment.
This is a repository copy of Advantages of virtual reality in the teaching and training of radiation protection during interventions in harsh environments.
In 2024, The Large Hadron Collider (LHC) at CERN will be upgraded to increase its luminosity by a factor of 10 (HL-LHC). The ATLAS inner detector (ITk) will be upgraded at the same time. It has suffered the most radiation damage, as it is the section closest to the beamline, and the particle collisions. Due to the risk of excessive radiation doses, human intervention to decommission the inner detector will be restricted. Robotic systems are being developed to carry out the decommissioning and limit radiation exposure to personnel. In this paper, we present a study of the radiation tolerance of a robotic finger assessed in the Birmingham Cyclotron facility. The finger was part of the Shadow Grasper from Shadow Robot Company, which uses a set of Maxon DC motors.
This paper presents a novel way to predict radiation dose using immersive Virtual Reality (VR). The platform allows an assessment of proposed interventions in as much detail and time as required. Its purpose is to give users the maximum amount of agency while in the environment. Workers get a realistic experience practising jobs and supervisors can oversee the expected radiation doses for each intervention.
A proof of concept performed and showed the platform returned a comparable result to the real radiation exposure for a predefined route. The errors of the system are dependant on the dose map. With an accurate dose map, the system will produce reliable results.
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